Abstract
The internationalization of higher education institutions (HEIs) involve a set of activities aimed at providing an educational experience in an environment that integrates a global perspective. The internationalization of HEIs incorporates a diverse set of academic and extracurricular activities with emphasis on the development and innovation of curricula, academic/student/faculty exchange programs, technological assistance, intercultural training, international student recruitment, and joint research initiatives. Despite the several studies on the internationalization of HEIs, no single study addresses issues like local responsiveness and global integration. As such, the authors discuss how the internationalization of HEIs has evolved and how Bartlett and Ghoshal's strategies fit in the internationalization path of HEIs.

Abstract
Relation Extraction (RE) is part of Information Extraction (IE) and aims to obtain instances of semantic relations in textual documents. The countless possibilities of relations, the myriad of subjects, the difficulty in identifying emotions and the amount of unstructured and heterogeneous data, have challenged the researchers to define innovative and even more accurate methodologies. This paper presents the evaluation results obtained with a set of RE systems on identifying semantic relations in criminal police reports. We have evaluated different applications with documents in English and Portuguese. The results obtained give us useful insights to continue the research work, and to design the relation extraction system applied to related domain.

Abstract
This work proposes a biased random key genetic algorithm (BRKGA) for the integrated scheduling of manufacturing, transport, and storage/retrieval operations in flexible manufacturing systems (FMSs). Only recently, research on this problem has been reported; however, no heuristic approaches have yet been reported. The computational results show the BRKGA to be capable of finding good quality solutions quickly.

Abstract
The big number of Internet of Things (IoT) devices, the lack of interoperability and the low accessibility of many of them in a vast heterogenous landscape will make it very hard to design specific monitor, manage and security measures and apply specific mechanism to IoT networks. Administration tasks like reporting, performance analysis, and anomaly detection also depend on monitoring for decision making. For that purpose, a solution used in IoT networks must be scalable and interoperable. In this work, we are concerned with the design of a real time monitoring system for IoT networks. To do this, after studying the various traditional network monitoring solutions, we concluded that there are still several developments to be made to this type of mechanism. The design proposed will consider the specific architecture of an IoT network, the scalability and heterogeneity of this type of environment, and the minimization of the use of resources. To do so, we considered the various network monitoring methods available and select a flow monitoring solution in an IoT network. After the presentation of a workflow for flow monitoring on IoT networks, the workflow was tested. By doing analysis of flows, rather than packets, we concluded that this type of solution could be more scalable and interoperable than traditional packet-based network monitoring, make it suitable in an IoT environment.

Abstract
Dynamic logic is a powerful framework for reasoning about imperative programs. This paper extends previous work [9] on the systematic generation of dynamic logics from the propositional to the equational case, to capture ‘full-fledged’ imperative programs. The generation process is parametric on a structure specifying a notion of ‘weight’ assigned to programs. The paper introduces also a notion of bisimilarity on models of the generated logics, which is shown to entail modal equivalence with respect to the latter.

Abstract
In this paper, a method for estimation of human pose is proposed, making use of ToF (Time of Flight) cameras. For this, a YOLO based object detection method was used, to develop a top-down method. In the first stage, a network was developed to detect people in the image. In the second stage, a network was developed to estimate the joints of each person, using the image result from the first stage. We show that a deep learning network trained from scratch with ToF images yields better results than taking a deep neural network pretrained on RGB data and retraining it with ToF data. We also show that a top-down detector, with a person detector and a joint detector works better than detecting the body joints over the entire image.